I'm working on a Face Recognition program using `Opencv 3.1` with `Python 3` on `Linux` and I'm trying to increase recognition accuracy as much as I can.
**My issue:** The confidence values between *Person A* (myself) and *Person B* (a friend) are a bit too close. There is a "fair" amount of difference, but not enough to set a threshold without getting false positives/negatives. I wrote a script to recognize *Person A* over a set of images for *Person B* and calculate the average confidence so I could see how much they differ by, and I noticed that as the face size in Step 3 of Preprocessing (see below) **increased**, the difference **decreased**.
My expectation was that by **increasing** the face size, there would be more detail and thus the difference would **increase**. Detected face sizes in this case were roughly `1500x1500`.
**My question:** How can I improve face recognition accuracy?
Below is some information about my project. Thanks.
----------
Files used:
- OpenCV's Haar Cascade (`haarcascade_frontalface_default.xml`) with a `scaleFactor` of `1.1` and `minNeighbors` of `10` for detecting faces.
- Local Binary Patterns Histograms algorithm (`createLBPHFaceRecognizer`) for recognizing faces.
----------
Image information:
- Each `4928x3264`
- Same lighting conditions
- Different facial expressions
- Different angles (heads tilting /
facing different directions)
----------
Preprocessing Steps:
1. Cropping the face out of the whole
image
2. Converting it to grayscale
3. Resizing it to a "standard" size
4. Histogram Equalization to smooth out
lighting differences
5. Applying a Bilateral Filter to
smooth out small details
----------
Training Steps:
1. Preprocess raw images for a given
person
2. Train recognizer using preprocessed
faces
3. Save trained recognizer model to a
file
----------
Recognition Steps:
1. Load recognizer model from file
2. Take in image from either file or
webcam
3. Detect face
4. Preprocess face (see above)
5. Attempt recognition
↧
